Improving FCC plant performance with model reference adaptive control based on neural network

The current paper presents the research results obtained by the authors regarding the automation of the reactor - regenerator group associated with the catalytic cracking process, using a model reference adaptive control system based on neural network. The first part of the paper describes the conventional control structure of the reactor-regenerator group. The second part of paper presents neural network model reference adaptive control structure proposed for the reactor-regenerator group. The proposed NN-model reference adaptive controller can significantly improve the reactor-regenerator group behavior and force the system to follow the reference model and minimize the error between the model and plant output. The results of the simulation have shown comparable or even superior performance against to the specific use of conventional techniques.